DOCSLIB.ORG

Decision-‐ Theore C

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Decision-‐ Theore C Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Delibera(ve Agents Knowledge Representa(on: Decision- Theorec Vasant Honavar Ar3ficial Intelligence Research Laboratory College of Informaon Sciences and Technology Bioinformacs and Genomics Graduate Program The Huck Ins3tutes of the Life Sciences Pennsylvania State University [email protected] hHp://vhonavar.ist.psu.edu hHp://faculty.ist.psu.edu/vhonavar Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Outline • Combining beliefs and preferences • Basics of U3lity Theory • U3lity Func3ons • Decision Networks • Value of Informaon Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Decision-Theore3c Agent • Different states have different u3lity to the agent • U3lity func3on of a simple decision theore3c agent maps each state onto a real number (u3lity) • Ac3ons are chosen based on the expected u3lity of the resul3ng state • More general seng involves making complex decisions involving sequences of ac3ons Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Making Simple Decisions - Example Decision 3me for Agent Joe Sixpack Study S1 Party S 0 S2 Sleep S3 Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Combining Beliefs Desires • Raonal Behavior – Based on beliefs about the world, in par3cular, consequences of one’s ac3ons in a given state – Must cope with uncertainty • There is no way to know for sure the outcome of an ac3on because of par3al ignorance or inherently stochas3c effects of ac3ons – Must provide a means of comparing alternaves using a common currency • Partying on a Thursday night versus geng an A Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Should Bob buy insurance? • Bob is contemplang whether he should take insurance on a shipment from Amsterdam to St. Petersburg. • If the ship does not encounter a storm, the shipment arrives on 3me in St. Petersburg, and Bob will earn 10,000 rubles • If the ship encounters a storm, the shipment will be delayed and Bob will earn only 8000 rubles. • The Amsterdam underwriters want Bob to pay 800 rubles for a full coverage insurance policy • Should Bob buy the policy? Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Should Bob buy insurance? • Poten3al loss to Bob in the event of delayed arrival of shipment = 2000 rubles • Cost of insurance policy = 1000 rubles • Should Bob buy the policy? Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Should Bob buy insurance? • Depends • On what? – Probability of storm-related delay – If the storm is highly unlikely (say probability of storm ≈ 0.2) at that 3me of the year, perhaps not – If the storm is likely (say probability of storm ≈ 0.8) then perhaps – Can we translate this intui3on into a precise prescrip3on for decision making under uncertainty? Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Should Bob buy insurance? • Suppose Bob believes that the probability of a storm related delay ≈ 0.2 • Bob’s expected earnings in the absence of insurance = (0.2)(8000) + 0.8 (10,000) = 1600 + 8000 = 9600 rubles • Bob’s expected earnings if he purchases insurance = 10,000 – 1000 = 9000 rubles • Bob is perhaps beHer off without insurance than with it. Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Should Bob buy insurance? • Suppose Bob believes that the probability of a storm related delay ≈ 0.8 • Bob’s expected earnings in the absence of insurance = (0.8)(8000) + 0.2 (10,000) = 6400 + 2000 = 8400 rubles • Bob’s expected earnings if he purchases insurance = 10,000 – 1000 = 9000 rubles • Bob is beHer off with insurance than without it. Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory St. Petersburg Paradox • From Nicolas Bernoulli’s leer • Consider the following game – Peter flips a fair coin repeatedly un3l a head shows up and will give Paul: • $2k if the first head shows up on the kth flip – How much should Paul pay Peter to play this game? Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory St. Petersburg Paradox (cont) k k=∞ k" 1% E(payoff ) = ∑ 2 $ ' =1+1+! = ∞ k=1 # 2& • The expected payoff is infinite • Does this mean it is raonal for Paul to pay Peter any € finite amount (say $1 million) to play this game? Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory St Petersburg Paradox • How much should Paul be willing to pay Peter for a chance to play the game? • Expected payoff = infinity • But.. There is a risk of loss – Suppose Paul pays $1,000,000 to pay the game – Suppose the first head shows up on the 2nd toss – Paul will receive $4 and lose $999996 • Is the gamble worth the risk? • Depends.. Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory St Petersburg paradox • Is Paul’s gamble worth the risk of losing almost $1,000,000? • Depends • On what? – How much money Paul has to start with – The risk might be unacceptable if Paul’s en3re life savings is $1,000,000 – The risk might be perfectly reasonable if Paul has billions in the bank – If Paul is poor, he may be jus3fied in paying no more than two dollars, the minimum possible pay-off of the game Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Making simple decisions • Can we turn the previous intui3on into a recipe for decision making under uncertainty? • Von Neumann – Morgenstern’s soluon – MEU principle • Choose ac3ons that maximize expected u)lity of outcome – As evident from the St. Petersburg paradox, for most people, the u)lity of money is not a linear func3on of the amount of money Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Making simple decisions • Notaon – U(S) : u3lity of state S – S : snapshot of the world – A : ac3on of the agent – Result : i (A) ith outcome of (state resul3ng from doing) A – E : available evidence – Do(A) : execung A in current state S Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Making simple decisions • U3lity func3on – assigns a single number to each outcome – models the desirability of the state to an agent – combined with probability of each outcome resul3ng from an ac3on yields expected u3lity for ac3on leading to each outcome S1 Action A S2 s0 S1 Action B S2 Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Making Decisions • Expected U3lity EU (A| E) P(Result (A) | E, Do(A))U(Result (A)) = ∑ i i i • Maximum Expected U3lity(MEU) – Choose an ac3on which maximizes agent’s expected u3lity • Compu3ng requires a probabilis3c P(Resulti (A) | E, Do(A)) model of the world (Bayes Network) • Compu3ng the u3lity of a state may require search U(Resulti (A)) because it can be hard to tell how good a state is un3l we know where it would lead us Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Decision Theore3c Agent Principles of Artificial Intelligence, IST 597F, Fall 2014, (C) Vasant Honavar Pennsylvania State University College of Information Sciences and Technology Artificial Intelligence Research Laboratory Loeries LoHeries are used to model decision making scenarios LoHeries have a
Load more

Recommended publications
  • Dear Colleague Letter: OFR-NSF Partnership in Support of Research Collaborations in Finance Informatics
    National Science Foundation 4201 Wilson Boulevard Arlington, Virginia 22230 NSF 13-093 Dear Colleague Letter: Date: May 14, 2013 The Directorate for Computer and Information Science and Engineering (CISE) of the National Science Foundation (NSF) and the Office of Financial Research (OFR) of the Department of Treasury share an interest in advancing basic and applied research centered on Computational and Information Processing Approaches to and Infrastructure in support of, Financial Research and Analysis and Management (CIFRAM). The complexity of modern financial instruments presents many challenges in recognizing and regulating Systemic Risk. The topic has been the subject of a recent National Academy of Science Report titled "Technical Capabilities Necessary for Systemic Risk Regulation: Summary of a Workshop." The CISE directorate and the Computing Community Consortium have sponsored workshops on Knowledge Representation and Information Management for Financial Risk Management and on Next-Generation Financial Cyberinfrastructure aimed at identifying research opportunities and challenges in CIFRAM. NSF and OFR have established a collaboration (hereafter referred to as CIFRAM) to identify and fund a small number of exploratory but potentially transformative CIFRAM research proposals. The collaboration enables OFR to support a broad range of financial research related to OFR’s mission, including research on potential threats to financial stability. It also assists OFR with the goal of promoting and encouraging collaboration between the government, the private sector, and academic institutions interested in furthering financial research and analysis. The collaboration enables the NSF to nurture fundamental CISE research on a variety of topics including algorithms, informatics, knowledge representation, and data analytics needed to advance the current state of the art in financial research and analysis.
    [Show full text]
  • Calendar of Events APRIL 6–7 Workshop on Lexical Semantics Systems (WLSS–98)
    AI Magazine Volume 19 Number 1 (1998) (© AAAI) E-mail: [email protected] Calendar of Events APRIL 6–7 Workshop on Lexical Semantics Systems (WLSS–98). Pisa, Italy ■ Contact: Alessandro Lenci Scuola Normale Superiore College Park, MD 20742 Laboratorio di linguistica March 1998 Piazza dei Cavalieri 7 MARCH 23–27 56126 Pisa, Italy MARCH 16–20 Practical Application Expo-98. Voice: +39 50 509219 Fourth World Congress on Expert London. Fax: +39 50 563513 Systems. Mexico City, Mexico celi.sns.it/~wlss98 ■ Contact: Tenth International Symposium on Steve Cartmell Artificial Intelligence. Mexico City, Practical Application Company APRIL 6–8 Mexico PO Box 173, Blackpool 1998 International Symposium on ■ Sponsor: Lancs. FY2 9UN Physical Design (ISPD–98). Mon- ITESM Center for Artificial United Kingdom terey, CA Intelligence Voice: +44 (0)1253 358081 ■ Sponsors: ■ Contact: Fax: +44 (0)1253 353811 ACM SIGDA, in cooperation with Rogelio Soto E-mail: [email protected] IEEE Circuits and Systems Society Program Chair, ITESM www.demon.co.uk/ar/Expo98/ and IEEE Computer Society Centro de Inteligencia Artificial ■ Contact: Av. Eugenio Garza Sada #2501 Sur D. F. Wong Monterrey, N. L. 64849, Mexico Technical Program Chair, ISPD–98 Voice: (52–8) 328-4197 University of Texas at Austin Fax: (52-8) 328-4189 April 1998 Department of Computer Sciences E-mail: [email protected]. Austin, TX 78712 APRIL 1–4 itesm.mx E-mail: [email protected] Second European Conference on www.ee.iastate.edu/~ispd98 Cognitive Modeling (ECCM–98). MARCH 16–20 Nottingham, UK International Workshop on APRIL 14–17 ■ Intelligent Agents on the Internet Contact: Fourteenth European Meeting on and Web.
    [Show full text]
  • Technology and Engineering International Journal of Recent
    International Journal of Recent Technology and Engineering ISSN : 2277 - 3878 Website: www.ijrte.org Volume-7 Issue-5S2, JANUARY 2019 Published by: Blue Eyes Intelligence Engineering and Sciences Publication d E a n n g y i n g o e l e o r i n n h g c e T t n e c Ijrt e e E R X I N P n f L O I O t T R A o e I V N O l G N r IN n a a n r t i u o o n J a l www.ijrte.org Exploring Innovation Editor-In-Chief Chair Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal (M.P.), India Associated Editor-In-Chief Chair Dr. Dinesh Varshney Professor, School of Physics, Devi Ahilya University, Indore (M.P.), India Associated Editor-In-Chief Members Dr. Hai Shanker Hota Ph.D. (CSE), MCA, MSc (Mathematics) Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India Dr. Gamal Abd El-Nasser Ahmed Mohamed Said Ph.D(CSE), MS(CSE), BSc(EE) Department of Computer and Information Technology, Port Training Institute, Arab Academy for Science, Technology and Maritime Transport, Egypt Dr. Mayank Singh PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu- Natal, Durban, South Africa. Scientific Editors Prof.
    [Show full text]
  • Node Injection a Acks on Graphs Via Reinforcement Learning
    Node Injection Aacks on Graphs via Reinforcement Learning Yiwei Sun, , Suhang Wangx , Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar Pennsylvania State University fyus162, szw494, xut10 ,tuh45 ,[email protected] ABSTRACT (a) (b) (c) Real-world graph applications, such as advertisements and prod- uct recommendations make prots based on accurately classify the label of the nodes. However, in such scenarios, there are high incentives for the adversaries to aack such graph to reduce the node classication performance. Previous work on graph adversar- ial aacks focus on modifying existing graph structures, which is clean graph dummy attacker smart attacker infeasible in most real-world applications. In contrast, it is more practical to inject adversarial nodes into existing graphs, which can Figure 1: (a) is the toy graph where the color of a node repre- also potentially reduce the performance of the classier. sents its label; (b) shows the poisoning injection attack per- In this paper, we study the novel node injection poisoning aacks formed by a dummy attacker; (c) shows the poisoning in- problem which aims to poison the graph. We describe a reinforce- jection attack performed by a smart attacker. e injected ment learning based method, namely NIPA, to sequentially modify nodes are circled with dashed line. the adversarial information of the injected nodes. We report the results of experiments using several benchmark data sets that show the superior performance of the proposed method NIPA, relative to Recent works [12, 32, 35] have shown that even the state-of-the- the existing state-of-the-art methods. art graph classiers are susceptible to aacks which aim at adversely impacting the node classication accuracy.
    [Show full text]
  • AAAI-12 Conference Committees
    AAAI 2012 Conference Committees Chairs and Cochairs AAAI Conference Committee Chair Dieter Fox (University of Washington, USA) AAAI­12 Program Cochairs Jörg Hoffmann (Saarland University, Germany) Bart Selman (Cornell University, USA) IAAI­12 Conference Chair and Cochair Markus Fromherz (ACS, a Xerox Company, USA) Hector Munoz‐Avila (Lehigh University, USA) EAAI­12 Symposium Chair David Kauchak (Middlebury College, USA) Special Track on Artificial Intelligence and the Web Cochairs Denny Vrandecic (Institute of Applied Informatics and Formal Description Methods, Germany) Chris Welty (IBM Research, USA) Special Track on Cognitive Systems Cochairs Matthias Scheutz (Tufts University, USA) James Allen (University of Rochester, USA) Special Track on Computational Sustainability and Artificial Intelligence Cochairs Carla P. Gomes (Cornell University, USA) Brian C. Williams (Massachusetts Institute of Technology, USA) Special Track on Robotics Cochairs Kurt Konolige (Industrial Perception, Inc., USA) Siddhartha Srinivasa (Carnegie Mellon University, USA) Turing Centenary Events Chair Toby Walsh (NICTA and University of New South Wales, Australia) Tutorial Program Cochairs Carmel Domshlak (Technion Israel Institute of Technology, Israel) Patrick Pantel (Microsoft Research, USA) Workshop Program Cochairs Michael Beetz (University of Munich, Germany) Holger Hoos (University of British Columbia, Canada) Doctoral Consortium Cochairs Elizabeth Sklar (Brooklyn College, City University of New York, USA) Peter McBurney (King’s College London, United Kingdom)
    [Show full text]
  • An Incremental Learning Algorithm with Confidence Estimation For
    990 ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 51, no. 8, august 2004 An Incremental Learning Algorithm with Confidence Estimation for Automated Identification of NDE Signals Robi Polikar, Member, IEEE, Lalita Udpa, Senior Member, IEEE, Satish Udpa, Fellow, IEEE, and Vasant Honavar, Member, IEEE Abstract—An incremental learning algorithm is intro- • applications calling for analysis of large volumes of duced for learning new information from additional data data; and/or that may later become available, after a classifier has al- • applications in which human factors may introduce ready been trained using a previously available database. The proposed algorithm is capable of incrementally learn- significant errors. ing new information without forgetting previously acquired knowledge and without requiring access to the original Such NDE applications are numerous, including but are database, even when new data include examples of previ- not limited to, defect identification in natural gas trans- ously unseen classes. Scenarios requiring such a learning mission pipelines [1], [2], aircraft engine and wheel compo- algorithm are encountered often in nondestructive evalua- nents [3]–[5], nuclear power plant pipings and tubings [6], tion (NDE) in which large volumes of data are collected in batches over a period of time, and new defect types may [7], artificial heart valves, highway bridge decks [8], optical become available in subsequent databases. The algorithm, components such as lenses of high-energy laser generators named Learn++, takes advantage of synergistic general- [9], or concrete sewer pipelines [10] just to name a few. ization performance of an ensemble of classifiers in which A rich collection of classification algorithms has been each classifier is trained with a strategically chosen subset of the training databases that subsequently become avail- developed for a broad range of NDE applications.
    [Show full text]
  • Conference Program Contents AAAI-14 Conference Committee
    Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14) Twenty-Sixth Conference on Innovative Applications of Artificial Intelligence (IAAI-14) Fih Symposium on Educational Advances in Artificial Intelligence (EAAI-14) July 27 – 31, 2014 Québec Convention Centre Québec City, Québec, Canada Sponsored by the Association for the Advancement of Artificial Intelligence Cosponsored by the AI Journal, National Science Foundation, Microso Research, Google, Amazon, Disney Research, IBM Research, Nuance Communications, Inc., USC/Information Sciences Institute, Yahoo Labs!, and David E. Smith In cooperation with the Cognitive Science Society and ACM/SIGAI Conference Program Contents AAAI-14 Conference Committee AI Video Competition / 7 AAAI acknowledges and thanks the following individuals for their generous contributions of time and Awards / 3–4 energy to the successful creation and planning of the AAAI-14, IAAI-14, and EAAI-14 Conferences. Computer Poker Competition / 7 Committee Chair Conference at a Glance / 5 CRA-W / CDC Events / 4 Subbarao Kambhampati (Arizona State University, USA) Doctoral Consortium / 6 AAAI-14 Program Cochairs EAAI-14 Program / 6 Carla E. Brodley (Northeastern University, USA) Exhibition /24 Peter Stone (University of Texas at Austin, USA) Fun & Games Night / 4 IAAI Chair and Cochair General Information / 25 David Stracuzzi (Sandia National Laboratories, USA) IAAI-14 Program / 11–19 David Gunning (PARC, USA) Invited Presentations / 3, 8–9 EAAI-14 Symposium Chair and Cochair Posters / 4, 23 Registration / 9 Laura
    [Show full text]
  • Adversarial Attacks on Graph Neural Networks Via Node Injections: a Hierarchical Reinforcement Learning Approach
    Adversarial Atacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach Yiwei Sun Suhang Wang∗ Xianfeng Tang The Pennsylvania State University The Pennsylvania State University The Pennsylvania State University University Park, PA, USA University Park, PA, USA University Park, PA, USA [email protected] [email protected] [email protected] Tsung-Yu Hsieh Vasant Honavar The Pennsylvania State University The Pennsylvania State University University Park, PA, USA University Park, PA, USA [email protected] [email protected] ABSTRACT ACM Reference Format: Graph Neural Networks (GNN) ofer the powerful approach to node Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant classifcation in complex networks across many domains including Honavar. 2020. Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. In Proceedings social media, E-commerce, and FinTech. However, recent studies of The Web Conference 2020 (WWW ’20), April 20–24, 2020, Taipei, Taiwan. show that GNNs are vulnerable to attacks aimed at adversely im- ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3366423.3380149 pacting their node classifcation performance. Existing studies of adversarial attacks on GNN focus primarily on manipulating the 1 INTRODUCTION connectivity between existing nodes, a task that requires greater Graphs, where nodes and their attributes denote real-world entities efort on the part of the attacker in real-world applications. In con- (e.g., individuals) and links encode relationships (e.g., friendship) trast, it is much more expedient on the part of the attacker to inject between entities, are ubiquitous in many application domains, in- adversarial nodes, e.g., fake profles with forged links, into existing cluding social media [1, 21, 35, 49, 50], e-commerce[16, 47], and graphs so as to reduce the performance of the GNN in classifying FinTech [24, 33].
    [Show full text]
  • Arxiv:1909.00384V2 [Cs.LG] 8 Aug 2020 1 Introduction
    DEEPHEALTH:REVIEW AND CHALLENGES OF ARTIFICIAL INTELLIGENCE IN HEALTH INFORMATICS APREPRINT Gloria Hyunjung Kwak Pan Hui Department of Computer Science and Engineering Department of Computer Science and Engineering The Hong Kong University of Science and Technology The Hong Kong University of Science and Technology Hong Kong Hong Kong [email protected] [email protected] August 11, 2020 ABSTRACT Artificial intelligence has provided us with an exploration of a whole new research era. As more data and better computational power become available, the approach is being implemented in various fields. The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare. It can help clinicians diagnose disease, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes or treatment recommendations, and predict infectious disease outbreaks with high accuracy. In contrast to traditional models, recent artificial intelligence approaches do not require domain-specific data pre-processing, and it is expected that it will ultimately change life in the future. Despite its notable advantages, there are some key challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label, bias) and model (reliability, interpretability, feasibility, security, scalability) for practical use. This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research. We highlight ongoing popular approaches’ research and identify several challenges in building models.
    [Show full text]
  • Accelerating Science a Grand Challenge for AI?
    Accelerating Science A Grand Challenge for AI? CCC Task Force on Convergence of Data and Computing Vasant Honavar, Mark Hill, Kathy Yelick Presentation to DARPA Defense Science Office March 16, 2017 Computing Community Consortium The mission of Computing Research Association's Computing Community Consortium (CCC) is to catalyze the computing research community and enable the pursuit of innovative, high-impact research. Promote Audacious Thinking: Computing Research Community Open Community Initiated Visioning Workshops National Agency Blue Sky Visioning Blue Sky Ideas tracks at conferences Priorities Requests Ideas Calls Inform Science Policy Outputs of visioning activities Council-Led Community Task Forces – e.g., Artificial Intelligence, Data Workshops Visioning and Computing, Health, Internet of Things, Privacy Reports • White Papers Engage the Community: Roadmaps • New Leaders CCC Blog - http://cccblog.org/ Computing Research in Action Videos Funding Science Policy Public Research “Highlight of the Week” Agencies Leadership Promote Leadership and Service: Computing Innovation Fellows Project Leadership in Science Policy Institute Accelerating Science: A Grand Challenge for AI? • Discussion based in part on: – Accelerang Science: A Compu3ng Research Agenda. A Compu3ng Community Consor3um White Paper, Vasant Honavar, Mark Hill, and Katherine Yelick, 2016 hJp://cra.org/crn/2016/03/3501/ – AAAI Fall Symposium on Accelerang Science: A Grand Challenge for AI • Other related events: – NSF Workshop on Discovery Informacs, February 2012 –
    [Show full text]
  • BRAIN-Lightning-Introductions-Slides
    Lightning Introductions Research Interfaces between Brain Science and Computer Science December 3-5, 2014 Charles Anderson / Colorado State Brain-Computer Interfaces www.cs.colostate.edu/eeg Sanjeev Arora / Princeton Computational complexity, designing algorithms for NP-hard problems, provably correct and efficient algorithms for ML (esp. unsupervised learning) (Panel Moderator: Computing and the brain.) Satinder Singh Baveja / Michigan • Reinforcement Learning: Architectures for converting payoffs/rewards to closed-loop behavior in AIs and Humans • Optimal rewards theory, or, Where do reward (functions) come from? • Computationally Rational models for explaining animal behavior and for deriving brain mechanisms. Andrew Bernat / CRA How might computer science and brain science get the resources they need? Matt Botvinick / Princeton •Cognitive/computational neuroscience • fMRI, behavioral methods, neurophysiology • Computational modeling (deep learning, reinforcement learning, graphical models) •Perspective: Computation as a Rosetta Stone •A common language in which to understand both behavior/cognition and neural function Randal Burns / JHU CS->Brain: Data-Intensive Web-services • scale infrastructure to capture high-throughput imaging (1TB/day) • integrated visualization and analytics • semantic/spatial queries of brain structure/function BRAIN->CS: Inspiration for new data organization and indexing techniques Miyoung Chun / Kavli Foundation The benefits are mutual: • Understanding the brain will have tremendous impacts on • hardware
    [Show full text]
  • Faculty Early Career Development
    Core Techniques and Technologies for Advancing Big Data Science & Engineering (BIGDATA) NSF 12-499 Vasant Honavar Program Director Information & Intelligent Systems (IIS) Division Computer and Information Science and Engineering (CISE) Directorate National Science Foundation BIGDATA Webinar Direct questions to [email protected] Vasant Honavar, May 2012 1 Big Data Research and Development Initiative • Big Data Senior Steering Group – chartered in spring 2011 under the Networking and Information Technology R&D (NITRD) Program – Members from DARPA, DOD OSD, DHS, DOE-Science, HHS, NARA, NASA, NIST, NOAA, NSA, and USGS – Co-chaired by NIH and NSF • White House Big Data Launch – March 29, 2012 • Long-term, National Big Data R&D with four major components: – Foundational research to develop new techniques and technologies to derive knowledge from data – New cyberinfrastructure to manage, curate, and serve data to research communities – New approaches for education and workforce development – Challenges and competitions to create new data analytic ideas, approaches, and tools from a more diverse stakeholder population BIGDATA Webinar Direct questions to [email protected] Vasant Honavar, May 2012 2 The Big Data Team Suzi Iacono, NSF, Karin Remington, NIH NITRD Big Data Steering Group • Vasant G. Honavar, NSF - CISE • Peter Lyster, NIH - NIGMS • Jia Li, NSF - MPS • Karin A. Remington, NIH - NIGMS • Dane Skow, NSF – OCI • Jerry Li, NIH - NCI • Peter H. McCartney, NSF - BIO • Vinay M. Pai, NIH - NIBIB • Doris L. Carver, NSF - EHR • Karen Skinner, NIH
    [Show full text]